Biopsychosocial risk factors for subjective cognitive decline among older adults during the COVID-19 pandemic: a population-based study
Bibliographic record
Abstract
OBJECTIVES: There have been concerns that the COVID-19 pandemic and the measures used to contain it impacted the cognitive health of older adults. We therefore examined the prevalence of subjective cognitive decline, and its associated risk factors and health consequencs, among dementia-free older adults 2 years into the pandemic in Switzerland. STUDY DESIGN: Population-based cohort study. METHODS: Prevalence of SCD was estimated using the cognitive complaint questionnaire administered to adults aged ≥65 years in June-September 2022 (Specchio-COVID19 cohort, N = 1414), and compared to prepandemic values from 2014 to 2018 (CoLaus|PsyCoLaus cohort, N = 1181). Associated risk factors and health consequences were assessed using logistic and/or linear regression. RESULTS: Prevalence of SCD in 2022 (18.9% [95% CI, 16.2-21.9]) was comparable to prepandemic levels in 2014-2018 (19.5% [17.2-22.1]). Risk factors included established risks for dementia-namely health issues, health behaviours, and depressive symptoms. Self-reported post-COVID, perceived worsening of mental health since the start of the pandemic, less frequent social club attendance, and increased loneliness were also risk factors for SCD. In turn, SCD was associated with poorer objective cognitive performance, difficulty performing instrumental activities of daily living, greater risk of falls, and lower well-being at one-year follow-up. CONCLUSIONS: While the overall prevalence of SCD in 2022 was comparable to prepandemic levels, we identified several pandemic-related risk factors for SCD, including perceived worsening of mental health and increased isolation since the start of the pandemic. These findings highlight the importance of mental health promotion strategies in reducing cognitive complaints and preventing cognitive decline.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".