Impact of loneliness on cancer mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: Loneliness and social isolation can have profound emotional and psychological impact on patients with cancer. Previous studies suggest loneliness may adversely impact cancer prognosis and survival. Potential mechanisms include a variety of biological, psychological and social factors, from impaired ability to access treatment to immune dysregulation. However, its impact is not clearly defined. We conducted the first systematic review and meta-analysis to investigate mortality in relation to loneliness and social isolation among cancer populations. Methods and analysis: We systematically searched MEDLINE, Embase and PsycINFO until 13 September 2024, for studies reporting on the impact of loneliness or social isolation on all-cause and cancer mortality in patients with cancer. Three reviewers independently screened studies, extracted data and assessed bias. A random-effects meta-analysis was used to examine associations between loneliness/social isolation and all-cause and cancer mortality. Results: Of 12 602 citations, 16 studies met eligibility criteria and 13 were included in the meta-analysis. Social isolation and loneliness were most frequently measured using the Social Network Index and UCLA Loneliness Scale, respectively. The median sample size was 6248, with a mean participant age of 63 years. Meta-analysis demonstrated loneliness/social isolation was associated with increased all-cause mortality (HR (95% CI)=1.34 (1.26 to 1.42), p<0.001) and cancer-specific mortality (HR (95% CI)=1.11 (1.02 to 1.21), p=0.014). Conclusion: Loneliness and social isolation may be associated with increased all-cause and cancer-specific mortality in patients with cancer. If these findings are confirmed by future, more definitive studies, together they support the need to incorporate psychosocial assessments and targeted interventions into cancer care to improve patient outcomes. PROSPERO registration number: CRD42024590482.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".