Loneliness and Healthcare Use in Older Adults: Evidence From a Nationally Representative Cohort in Northern Ireland—A Cross-Sectional Replication Study
Bibliographic record
Abstract
Background: Few have explored associations between loneliness and healthcare use independent of health and health behaviors. Recent indication of gender effects also requires validation across health service and cultural settings. We investigated the associations among loneliness, health and healthcare use (HCU) in older adults including stratification to investigate whether associations differed by gender. Methods: Secondary analysis of a nationally representative sample of 8,309 community-dwelling adults aged 50 and over from the Northern Ireland Cohort for the Longitudinal Study of Aging. Primary outcomes were: self-reported General Practice (GP) and emergency department (ED) visits in past year. Negative binomial and logistic regression analysis were used to investigate associations between loneliness and HCU, later adjusting for potential confounders (health and health behaviors). Results: Loneliness was consistently positively associated with both GP and ED visits (with IRRs ranging from 1.10 to 1.49 for GP visits, 1.16 to 1.98 for ED visits and ORs ranging from 1.13 to 1.51 for reporting at least one ED visit). With addition of health and health behaviors, all associations between loneliness and HCU became non-significant, excepting a small independent association between UCLA score and GP visits [IRR 1.03 (95% CI 1.01–1.05)]. Stratification of models revealed no gender effects. Conclusion: All but one association between loneliness and HCU became non-significant when health and health behaviors were included. The remaining association was small but implications remain for health service resources at population level. No gender effects were present in contrast to recent findings in the Republic of Ireland. Further studies on gender, loneliness and healthcare use needed.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".