High burden of neurological disease in the older general population: results from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our objective was to study the association between the presence of a neurological disease and the comorbidity burden as well as healthcare utilization (HCU). METHODS: Using baseline data from the Canadian Longitudinal Study on Aging (CLSA), we examined the burden of five neurological conditions. The CLSA is a population-based study of approximately 50 000 individuals, aged 45-85 years at baseline. We used multivariable Poisson regression to identify correlates of comorbidity burden and HCU. RESULTS: The lifetime prevalence of five neurological diseases is presented: epilepsy, Parkinson's disease/parkinsonism, stroke/transient ischaemic attack, multiple sclerosis and migraine. We found the somatic and psychiatric comorbidity burden to be higher in those individuals with a neurological disease (an 18-45% mean increase in the number of chronic conditions) as compared with the comparison group without a neurological disease, except for Parkinson's disease/parkinsonism. The presence of a neurological disease was associated with only a modest increase in the probability of visiting a general practitioner but was associated with a greatly increased probability of visiting a medical specialist (up to 68% more likely) or an emergency department (up to 79% more likely) and an overnight hospitalization (up to 108% more likely). CONCLUSIONS: We found striking associations between our neurological diseases and increased comorbidity burdens and HCU. These findings are important for informing public policy planning as well as driving avenues for future research. The present study established the CLSA as an important research platform for the study of neurological conditions in an aging general population.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".