Associations of social isolation and loneliness with healthcare utilization among older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Background and Objectives: Social isolation and loneliness are significant public health concerns associated with increased healthcare utilization among older adults. This review aims to synthesize evidence on the associations between social isolation, loneliness, and healthcare utilization. Research Design and Methods: Five databases were searched from inception to March 21, 2025, using keyword groups related to social isolation/loneliness, older adults, and healthcare utilization (primary care, emergency visits, inpatient care, and outpatient care). Methodological quality was assessed using the Newcastle-Ottawa Scale. Random-effects models were employed to pool effect sizes (incidence rate ratios [IRRs], odds ratios [ORs]). Results: = 309,023). Due to insufficient data, meta-analyses for the association between social isolation and primary care or outpatient care utilization were not conducted. Social isolation was statistically associated with increased inpatient care utilization (IRRs = 1.37, 95% CI: 1.24-1.53) but not with emergency department visits. For loneliness, meta-analyses for outpatient care were not feasible due to limited studies. Loneliness was statistically associated with increased emergency department visits (IRRs = 1.15, 95% CI: 1.06-1.24) and inpatient care utilization (OR = 1.13, 95% CI: 1.07-1.20) but not with primary care use. Discussion and Implications: This is the first meta-analysis to comprehensively synthesize the associations between social isolation, loneliness, and 4 types of healthcare utilization among older adults. The findings highlight the importance of addressing social isolation and loneliness as potential strategies to reduce avoidable healthcare utilization.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| 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".