The Relationship between Multimorbidity and Incident Dementia in Ontario
Bibliographic record
Abstract
It has been suggested that dementia is currently the greatest global challenge for health and social care. Given projected increases in prevalence, coupled with a lack of effective treatment, the primary prevention of dementia is being recognized for its potential role in reducing future disease burden. However, for preventive strategies to ultimately be tailored to individuals, there are gaps in our understanding of dementia that must first be addressed. In this context, this thesis examines three research questions related to the risk of incident dementia using linked provincial health administrative data: 1. Are all risk factors equal in how they contribute to dementia risk and how do the effects of multiple chronic conditions compare to the effects of chronic conditions considered as risk conditions for dementia? (Study 1) 2. When the role of time is appropriately accounted for, is there evidence of synergistic and dose-response effects of diabetes and hypertension and what role does medical comorbidity play? (Study 2) 3. Is there evidence of sex differences in the associations of diabetes, hypertension, and medical comorbidity with dementia? (Study 3) Study 1 confirmed a dose-response relationship between increased risk factor exposure and an increasing risk of dementia. However, it also shed light on the unequal contribution of risk factors to risk and highlighted a strong association between multimorbidity and dementia. Study 2 revealed evidence of a dose-response relationship between diabetes duration and risk of dementia. Hypertension was only associated with dementia in the presence of diabetes. Strong cumulative effects were found for medical comorbidity (i.e., number of chronic conditions other than diabetes and hypertension). There was also evidence that risk condition combination (i.e., the presence of diabetes and/or hypertension) and medical comorbidity each modified the association of the other with dementia. Study 3 did not find evidence of sex differences in the associations of risk condition combination and medical comorbidity with dementia. The thesis aimed to capture a slice of the nuances in health status that may contribute to heterogeneity in dementia risk. The results underscore that a holistic, lifecourse approach is required for understanding dementia risk and its prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".