Patterns of health service use in community living older adults with dementia and comorbid conditions: a population-based retrospective cohort study in Ontario, Canada
Bibliographic record
Abstract
Abstract Background Patients with dementia have increased healthcare utilization and often have comorbid chronic conditions. It is not clear if the increase in utilization is driven by dementia, the comorbidities or both. The objective of this study was to describe the number and types of comorbid conditions in a population-based cohort of older adults with dementia and how the level of comorbidity impacts dementia-related and non-dementia-related health service utilization. Methods This study is a retrospective cohort study using multiple linked administrative databases to examine health service utilization and costs of 100,630 community-living older adults living with pre-existing dementia in Ontario, Canada. Comorbid conditions and health service utilization were measured using administrative data (physician visits, emergency department visits, hospitalizations, and homecare contacts). Results Nearly all, 96.3Â %, had at least one comorbid condition, while 18.4Â % had five or more comorbid conditions. The most common comorbid conditions were hypertension (77.8Â %), and arthritis (66.2Â %). All types of utilization increased consistently with the number of comorbid conditions. The average number of dementia-related services tended to be similar across all levels of comorbidity while the average number of non-dementia related visits tended to increase with the level of comorbidity. Conclusions Comorbidities in community-living older adults with dementia are common and account for a substantial proportion of health service use and costs in this population. Our results suggest that comprehensive programs that take a holistic view to identify the needs of patients in the context of other comorbidities are required for persons with dementia living in the community.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".