Temporal association between loneliness and polypharmacy: Analyses of the Canadian Longitudinal Study of Aging
Bibliographic record
Abstract
PURPOSE: Although loneliness is cross-sectionally associated with polypharmacy, its association over time is unknown. We examined whether loneliness was associated with incident polypharmacy and greater medication use over time, and whether this association was attenuated by mood and anxiety disorders. METHODS: We analyzed baseline and 3-year follow-up data from the Canadian Longitudinal Study of Aging (CLSA) comprehensive cohort. Loneliness was defined as responding "all the time" or "occasionally" to the question, "How often did you feel lonely?" We ascertained the association between loneliness at baseline and polypharmacy (defined as ≥5 medications) and change in number of medications at follow-up from multivariable modified Poisson and linear regression models, with and without adjusting for mood and anxiety disorders. RESULTS: We included 29,968 participants (mean age 62.9 years, 50.9 % female); 3244 (10.8 %) were lonely at baseline. Among participants without polypharmacy at baseline (n = 16,075), participants who were lonely were more likely to have incident polypharmacy at follow-up than those who were not lonely (1453 [51.1 %] vs. 10,188 [41.3 %], aRR 1.16 [1.05-1.28]). Participants who were lonely increased medication use more than those who were not lonely (mean change 0.06 [standard deviation 3.0] vs. 0.04 [2.7], adjusted difference = 0.13 [0.04-0.23]). These associations were attenuated and became non-significant after adjusting for mood and anxiety disorders. CONCLUSIONS: Loneliness was associated with an increase in risk of polypharmacy and the number of medications used over time, but mood disorders and anxiety attenuated this effect. Using multimodal interventions targeting mood disorders and anxiety may help mitigate loneliness-related polypharmacy.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".