What are the most prevalent combinations of modifiable dementia risk factors that can be targeted in multidomain lifestyle intervention in Canada?
Bibliographic record
Abstract
Abstract Background Not all risk factors have the same weight in dementia development, and the combinations of common risk factors differs across individuals and populations. The goal of this study is to estimate the potential population impact of modifiable risk factors for dementia in Canada, and to identify the combinations of the modifiable risk factors that could prevent many dementia cases in Canadian adults. Method This is a secondary analysis of data from the Canadian Longitudinal Study on Aging Comprehensive Cohort (n = 30,009). The prevalence and population attributable fraction (PAF) of 12 modifiable lifestyle risk factors established in the Lancet Commission Report 2020 were calculated using baseline data. The prevalence of every possible dyad, triad, and tetrad combination of the risk factors was estimated. All analyses were stratified by 2 sex strata (male, female) and 4 age strata (45‐54, 55‐64, 65‐74, 75+). Result The combined PAF of the 12 lifestyle risk factors was 49% in the Canadian population. The single risk factors that contributed the most were physical inactivity (11.3%), hearing loss (6.9%), and obesity (6.9%) in Canada, whereas social isolation (0.4%), alcohol (1.1%), and smoking (1.4%) had substantially smaller effects (Table 1). The most prevalent risk factor dyad was physical inactivity and sleep disturbance (34%). Obesity, physical inactivity, and sleep disturbance was the most prevalent risk factor triad (14%). The tetrad of hearing loss, obesity, physical inactivity, and sleep disturbance had the highest prevalence (4.9%). The prevalent risk factor combinations were similar in the two younger age groups and both sexes, but not in the two older age groups (Table 2). Conclusion Nearly 50% of dementia cases in Canada can be prevented by modifying 12 lifestyle risk factors. The most prevalent combinations of risk factors differ before and after the age of 65, but not by sex. The most prevalent risk factor combinations consist of physical inactivity, sleep disturbance and obesity in the two younger age groups, and hearing loss, sleep disturbance and obesity in the two older age groups. These findings may help strategically target tailoring multidomain dementia intervention programs to have the greatest impact in the Canadian population.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".