Trends in site of death and health care utilization at the end of life: a population-based cohort study
Bibliographic record
Abstract
Background: High rates of health care utilization at the end of life may be a marker of care that does not align with patient-stated preferences. We sought to describe trends in end-of-life care and factors associated with dying in hospital. Methods: We conducted a population-level retrospective cohort study of adult decedents in Ontario between Apr. 1, 2004, and Mar. 31, 2015, using linked administrative data sets, including the Office of the Registrar General for Deaths database, the hospital Discharge Abstract Database, the National Ambulatory Care Reporting System and physicians’ billing claims (Ontario Health Insurance Plan). The primary outcome was place of death. To determine health care utilization and health care costs during the 6 months before death, we also identified admissions to hospital and to the intensive care unit, emergency department visits, and receipt of mechanical ventilation and palliative care. Results: In the last 6 months of life, 77.3% of 962 462 decedents presented to an emergency department, 68.4% were admitted to hospital, 19.4% were admitted to an intensive care unit, and 13.9% received mechanical ventilation. Forty-five percent of all deaths occurred in hospital, a proportion that declined marginally over time, whereas receipt of palliative care increased during terminal hospital admissions (from 14.0% in fiscal year 2004/05 to 29.3% in 2014/15, p < 0.001) and in the last 6 months of life (from 28.1% in 2004/05 to 57.7% in 2014/15, p < 0.001). The proportion of decedents who presented to the emergency department, were admitted to hospital or were admitted to the intensive care unit in the last 6 months of life did not change over 11 years. The mean total health care costs in the last 6 months of life were highest among those dying in hospital, with most costs attributable to inpatient medical care. Interpretation: Health care utilization in the last 6 months of life was substantial and did not decrease over time. It is possible that increased capacity for palliative, hospice and home care at the end of life may help to better align health system resources with the preferences of most patients, a topic that should be explored in future studies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".