Development and validation of a dementia risk index for use with administrative healthcare data in Canada
Bibliographic record
Abstract
Abstract Background Identification of individuals at high risk for dementia is important for planning health services and targeting prevention strategies. While dementia risk indices are available with clinical data, few have been created for use with population‐based administrative data in Canada. The purpose of this study was to establish a dementia risk index for people aged 50 years and older in Alberta, Canada, utilizing routinely gathered administrative healthcare databases. Using population‐based data, we can better understand the risk of dementia to identify individuals and populations at greatest risk. Method The study population included all individuals in Alberta, Canada, aged 50 years or older and free of diagnosed dementia on April 1, 2013. Participants were followed for up to 5 years to assess new diagnoses of dementia using a validated algorithm. A total of 59 known measurable dementia risk factors were assessed as potential predictors of dementia. At a 3:2 ratio, the sample was randomly divided into derivation and validation sets. The risk prediction model was created using a Cox proportional hazard model from a list of candidate variables in the derivation sample, and its performance to predict dementia was evaluated in the validation sample. Result One million participants aged 50 years and older without dementia were included in the study sample (mean age 62.95 years, 50.52% female), and 41,834 (3.86%) individuals developed dementia during follow‐up (mean duration 4.78 years). Out of 59 candidate variables, 17 variables were identified as independent risk factors for dementia, including advanced age, female gender, increased overall medical comorbidity, neighborhood measures of socioeconomic deprivation, specific medical conditions, mental health diagnoses, and measures of health service utilization. The final multivariate Cox proportional hazards model demonstrated good discriminative ability (Harrel’s C‐statistic = 0.87). Conclusion This risk prediction index, derived using population‐based data, can reliably identify Canadian adults at increased risk of dementia. This risk index may assist clinicians, and the general public in assessing their risks for dementia and facilitate discussions on the most effective ways to reduce this risk. Understanding individual or population‐based dementia risk differences can help design dementia prevention and health promotion programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".