Predictive role of loneliness on mortality before the age 85 years among mid- to later-life adults in the United States: a 10-year retrospective cohort study
Bibliographic record
Abstract
AIMS: Loneliness is a common public health concern, particularly among mid- to later-life adults. However, its impact on early mortality (deaths occurring before reaching the oldest old age of 85 years) remains underexplored. This study examined the predictive role of loneliness on early mortality across different age groups using data from the Health and Retirement Study (HRS). METHODS: A retrospective cohort study was conducted using data from the 2010-2020 waves of the HRS, restricted to participants aged 50-84 years at baseline. Loneliness was measured using the 11-item UCLA Loneliness Scale, categorized into four levels: low/no loneliness (scores 11-13), mild loneliness (14-16), moderate loneliness (17-20) and severe loneliness (21-33). Cox proportional hazards models and time-varying Cox regression models with age as the time scale were created to evaluate the relationship between loneliness and early mortality, adjusting for sociodemographic, lifestyle, and physical and mental health factors. RESULTS: Among 6,392 participants, the overall mortality rate before the age of 85 years was 19.1 per 1,000 person-years. A dose-response relationship was observed, with moderate and severe loneliness associated with 23% (adjusted hazard ratio [aHR]: 1.23, 95% confidence interval [CI] = 1.02-1.48) and 36% (aHR: 1.36, 95% CI = 1.13-1.65) higher mortality risk, respectively. Significant associations existed for the 65-74-year-old (aHR = 1.37, 95% CI = 1.03-1.83) and 75-84-year-old (aHR = 1.77, 95% CI = 1.23-2.56) age groups in the fully-adjusted models, but not for the 50-64-year-old age group. Time-varying Cox models showed a stronger association for severe loneliness (aHR = 1.65, 95% CI = 1.37-1.99). CONCLUSIONS: Loneliness is a significant predictor of mortality among older adults. Preventive and interventional programs targeting loneliness may promote healthy ageing.
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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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".