Increased prevalence of loneliness and associated risk factors during the COVID-19 pandemic: findings from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Abstract Background Older adults have been disproportionately impacted by COVID-19 and related preventative measures undertaken during the pandemic. Given clear evidence of the relationship between loneliness and health outcomes, it is imperative to better understand if, and how, loneliness has changed for older adults during the COVID-19 pandemic, and whom it has impacted most. Method We used “pre-pandemic” data collected between 2015–2018 (n = 44,817) and “during pandemic” data collected between Sept 29-Dec 29, 2020 (n = 24,114) from community-living older adults participating in the Canadian Longitudinal Study on Aging. Loneliness was measured using the 3-item UCLA Loneliness Scale. Weighted generalized estimating equations estimated the prevalence of loneliness pre-pandemic and during the pandemic. Lagged logistic regression models examined individual-level factors associated with loneliness during the pandemic. Results We found the adjusted prevalence of loneliness increased to 50.5% (95% CI: 48.0%-53.1%) during the pandemic compared to 30.75% (95% CI: 28.72%-32.85%) pre-pandemic. Loneliness increased more for women (22.3% vs. 17.0%), those in urban areas (20.8% vs. 14.6%), and less for those 75 years and older (16.1% vs. 19.8% or more in all other age groups). Loneliness during the pandemic was strongly associated with pre-pandemic loneliness (aOR 4.87; 95% CI 4.49–5.28) and individual level sociodemographic factors [age < 55 vs. 75 + (aOR 1.41; CI 1.23–1.63), women (aOR 1.34; CI 1.25–1.43), and no post-secondary education vs. post-secondary education (aOR 0.73; CI 0.61–0.86)], living conditions [living alone (aOR 1.39; CI 1.27–1.52) and urban living (aOR 1.18; CI 1.07–1.30)], health status [depression (aOR 2.08; CI 1.88–2.30) and having two, or ≥ three chronic conditions (aOR 1.16; CI 1.03–1.31 and aOR 1.34; CI 1.20–1.50)], health behaviours [regular drinker vs. non-drinker (aOR 1.15; CI 1.04–1.28)], and pandemic-related factors [essential worker (aOR 0.77; CI 0.69–0.87), and spending less time alone than usual on weekdays (aOR 1.32; CI 1.19–1.46) and weekends (aOR 1.27; CI 1.14–1.41) compared to spending the same amount of time alone]. Conclusions As has been noted for various other outcomes, the pandemic did not impact all subgroups of the population in the same way with respect to loneliness. Our results suggest that public health measures aimed at reducing loneliness during a pandemic should incorporate multifactor interventions fostering positive health behaviours and consider targeting those at high risk for loneliness.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".