Cross-National Evidence on Risk of Death Associated with Loneliness: A Survival Analysis of 1-Year All-Cause Mortality among Older Adult Home Care Recipients in Canada, Finland, and Aotearoa | New Zealand
Bibliographic record
Abstract
OBJECTIVES: To examine all-cause 1-year risk of mortality associated with loneliness for home care recipients after adjusting for potential confounders. DESIGN: Survival analyses with parallel designs using interRAI Home Care assessments and mortality. SETTINGS AND PARTICIPANTS: Home care recipients in 3 countries-Canada, Finland, and Aotearoa | New Zealand (ANZ)-who were 65 years and older were selected for this retrospective analysis. METHODS: We fit a multivariable Cox regression model to obtain the adjusted proportional hazards of 1-year mortality among home care recipients for each of the 3 countries. RESULTS: A total of 178,610, 35,073, and 169,703 home care recipients in Canada, Finland, and ANZ respectively, were included in the study. The respective baseline rates of loneliness in the 3 countries were 15.9%, 20.5%, and 24.4% of recipients. In multivariate Cox regression analysis, being lonely was independently associated with a lower likelihood of mortality among home care recipients, with hazard ratios of 0.82 (95% CI 0.78-0.86) in Canada, 0.85 (95% CI 0.79-0.92) in Finland, and 0.77 (95% CI 0.74-0.81) in ANZ. CONCLUSIONS AND IMPLICATIONS: Loneliness is pervasive in home care settings across the 3 countries; however, its association with mortality differs from reports for the general population. Loneliness was not associated with an increased risk of death after adjusting for health-related covariates. The causal order between changes in health, loneliness, and mortality is unclear. For example, loneliness may be a consequence of those health changes rather than their cause. Hence, temporal order needs better delineation. Health care systems should treat loneliness as an important mental health priority irrespective of a possible relationship with physical health.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".