Dying with Parkinson’s Disease: Healthcare Utilization and Costs in the Last Year of Life
Bibliographic record
Abstract
BACKGROUND: The end-of-life period is associated with disproportionately higher health care utilization and cost at the population level but there is little data in Parkinson's disease (PD). OBJECTIVE: The goals of this study were to 1) compare health care use and associated cost in the last year of life between decedents with and without PD, and 2) identify factors associated with palliative care consultation and death in hospital. METHODS: Using linked administrative datasets held at ICES, we conducted a retrospective, population-based cohort study of all Ontario, Canada decedents from 2015 to 2017. We examined demographic data, rate of utilization across healthcare sectors, and cost of health care services in the last year of life. RESULTS: We identified 291,276 decedents of whom 12,440 (4.3%) had a diagnosis of PD. Compared to decedents without PD, decedents with PD were more likely to be admitted to long-term care (52% vs. 23%, p < 0.001) and received more home care (69.0 vs. 41.8 days, p < 0.001). Receipt of palliative homecare or physician palliative home consultation were associated with lower odds of dying in hospital (OR: 0.24, 95% CI: 0.19- 0.30, and OR: 0.38, 95% CI: 0.33- 0.43, respectively). Mean cost of care in the last year of life was greater for decedents with PD ($68,391 vs. $59,244, p < 0.001). CONCLUSION: Compared to individuals without PD, individuals with PD have higher rates of long-term care, home care and higher health care costs in the last year of life. Palliative care is associated with a lower rate of hospital death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".