Multimorbidity and cognition among Canadian older adults: A three-decade perspective
Bibliographic record
Abstract
Background: Scarce information exists about whether the relationship between multimorbidity and cognitive decline has changed over the past decades.\nObjectives: 1) To summarize knowledge about the association of multimorbidity and cognitive decline by performing a systematic review. 2) To estimate the association between multimorbidity in two cohorts selected three decades apart. 3)To identify which multimorbidity combinations have the strongest associations with cognitive decline 4) To identify protective factors that reduce the risk of cognitive decline in the presence of multimorbidity.\nMethods: We performed a systematic review following the PRISMA statement. We then addressed objectives 2-4 by analyzing data from two longitudinal studies. The Canadian Study of Health and Aging (CSHA, n = 497) collected a baseline in 1991. The Canadian Longitudinal Study of Health and Aging (CLSA, n = 23654) had a baseline in 2015. Both studies collected information on several chronic conditions and used validated measures of different cognitive domains. Incident dementia was available in the CSHA only. Statistical models included multilevel linear and logistic regression and classification and regression trees (CART).\nResults: We identified 19 publications evaluating the relationship between multimorbidity and cognitive decline, of which 17 studies reported statistically significant results for this association. We found no association between multimorbidity and cognitive scores in the CSHA, while in the CLSA, we found associations with frontal function (ß:–0.049) and RAVLT (ß:–0.05). We did not find an association between multimorbidity and 5-year dementia incidence in the CSHA. In the CLSA, CART identified cardiopathies and stroke as part of the multimorbidity combinations associated with the lowest cognitive test scores. Finally, in the CSHA, physical activity decreased dementia risk (OR = 0.45, 95% CI: 0.20–0.98).\nConclusion: Our systematic review supports the association between multimorbidity and cognitive decline without evidence of change over time. We found an association of multimorbidity on cognitive scores only in the ongoing CLSA; future research should clarify this apparent increasing effect of multimorbidity on cognition. We noticed that multimorbidity combinations containing cardiopathies and stroke were associated with low cognitive scores. Finally, we found that regular exercise reduced the risk of dementia in multimorbidity patients.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".