Loneliness, social isolation, and living alone: a comprehensive systematic review, meta-analysis, and meta-regression of mortality risks in older adults
Bibliographic record
Abstract
Loneliness, social isolation, and living alone are significant risk factors for mortality, particularly in older adults. This systematic review and meta-analysis aimed to quantify their associations with all-cause and cause-specific mortality in older adults, broadening previous research by including more social factors. Comprehensive searches were conducted in PubMed, APA PsycINFO, and CINAHL until December 31, 2023, following PRISMA 2020 and MOOSE guidelines. Studies included were prospective cohort or longitudinal studies examining the relationship between loneliness, social isolation, living alone, and mortality. Quality was assessed using the Newcastle-Ottawa Scale. Meta-analyses used random-effects models with the Restricted Maximum Likelihood method. Subgroup and meta-regression analyses explored the relationships further. Of 11,964 identified studies, 86 met the inclusion criteria. Loneliness was associated with increased all-cause mortality (HR 1.14, 95% CI 1.10-1.18), with substantial heterogeneity (I² = 84.0%). Similar associations were found for social isolation (HR 1.35, 95% CI 1.27-1.43) and living alone (HR 1.21, 95% CI 1.13-1.30). Subgroup analyses revealed variations based on factors like sex, age, region, chronic diseases, and study quality. Meta-regression identified longer follow-up, female sex, validated social network indices, adjustments for cognitive function, and study quality as significant predictors of mortality risks. These findings highlight the need for public health interventions to address these social factors and improve health outcomes in older adults. However, further research is needed due to variability and heterogeneity across studies. Also studying the cumulative effect of these factors on mortality risks will be of considerable interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".