Prevalence and population attributable fractions of potentially modifiable risk factors for dementia in Canada: a cross-sectional analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract Background Identification and assessment of modifiable risk factors for dementia is a public health priority in Canada and worldwide. We investigated the prevalence and population attributable fraction (PAF) of 12 potentially modifiable risk factors for all-cause dementia in middle-aged and older Canadians. Methods We conducted a cross-sectional study of data from the Comprehensive cohort of the Canadian Longitudinal Study on Aging, a national sample of 30,097 individuals between the ages of 45 and 85 at baseline (2011-2015). Risk factors and associated relative risks were taken from a highly cited systematic review published by an international commission on dementia prevention. We estimated the prevalence of each risk factor using sampling weights to be more generalizable to the Canadian population. Individual PAFs were calculated both crudely and weighted for communality, and combined PAFs were calculated with methods reflecting both multiplicative and additive interaction assumptions. Analyses were additionally performed stratified by household income and were repeated at CSLA’s first three-year follow-up (2015-2018). Results The most prevalent risk factors at baseline were physical inactivity (63.8%; 95% CI, 62.8% – 64.9%), hypertension (32.8%; 31.7% – 33.8%), and obesity (30.8%; 29.7% – 31.8%). The highest crude PAFs were for physical inactivity (19.9%), traumatic brain injury (16.7%), and hypertension (16.6%). The highest weighted PAFs were for physical inactivity (11.6%), depression (7.7%), and hypertension (6.0%). We estimated that the 12 risk factors combined accounted for 43.4% (37.3%-49.0%) of dementia cases assuming weighted multiplicative interactions and 60.9% (55.7%-65.5%) assuming additive interactions. There was a clear gradient of increasing prevalence and PAF with decreasing income for 9 of the 12 risk factors. Interpretation The findings of this study can inform individual and population-level dementia prevention strategies in Canada, focusing efforts on risk factors with the largest impact on the number of dementia cases. Differences in the impact of individual risk factors between this study and other international and regional studies highlight the importance of tailoring national dementia strategies to the local distribution of risk factors.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".