Examining the prevalence and correlates of multimorbidity among community-dwelling older adults: cross-sectional evidence from the Canadian Longitudinal Study on Aging (CLSA) first-follow-up data
Bibliographic record
Abstract
INTRODUCTION: multimorbidity has become an increasingly important issue for many populations around the world, including Canada. The objectives of this study were to estimate the prevalence of multimorbidity at first follow-up and to identify factors associated with multimorbidity using data from the Canadian Longitudinal Study on Aging (CLSA). METHODS: this study included 27,701 community-dwelling participants in the first follow-up of the CLSA. Multimorbidity was operationalised using two definitions (Public Health and Primary Care), as well as the cut-points of two or more chronic conditions (MM2+) and three or more chronic conditions (MM3+). The prevalence of multimorbidity was calculated at first follow-up and multivariable regression models were used to identify correlates of multimorbidity occurrence. RESULTS: the prevalence of multimorbidity at first follow-up was 32.3% among males and 39.3% among females when using the MM2+ Public Health definition, whereas the prevalence was 67.2% among males and 75.8% among females when using the MM2+ Primary Care definition. Older age, lower alcohol consumption, lower physical activity levels, dissatisfaction with sleep quality, dissatisfaction with life and experiencing social limitations due to health conditions were significantly associated with increased odds of multimorbidity for both males and females, regardless of the definition of multimorbidity used. CONCLUSION: various sociodemographic, behavioural and psychosocial factors are associated with multimorbidity. Future research should continue to examine how the prevalence of multimorbidity changes with time and how these changes may be related to specific risk factors. This future research should be supplemented with studies examining the longitudinal impacts of multimorbidity over time.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".