Past trends and projections of hospital deaths to inform the integration of palliative care in one of the most ageing countries in the world
Bibliographic record
Abstract
BACKGROUND: Monitoring where people die is key to ensure that palliative care is provided in a responsive and integrated way. AIM: To examine trends of place of death and project hospital deaths until 2030 in an ageing country without integrated palliative care. DESIGN: Population-based observational study of mortality with past trends analysis of place of death by gender, age and cause of death. Hospital deaths were projected until 2030, applying three scenarios modelled on 5-year trends (2006-2010). SETTING/PARTICIPANTS: All adult deaths (⩾18 years old) that occurred in Portuguese territory from 1988 to 2010. RESULTS: There were 2,364,932 deceased adults in Portugal from 1988 to 2010. Annual numbers of deaths increased 11.1%, from 95,154 in 1988 to 105,691, mainly due to more than doubling deaths from people aged 85+ years. Hospital deaths increased by a mean of 0.8% per year, from 44.7% (n = 42,571) in 1988 to 61.7% (n = 65,221) in 2010. This rise was largest for those aged 85+ years (27.8% to 54.0%). Regardless of the scenario considered, and if current trends continue, hospital deaths will increase by more than a quarter until 2030 (minimum 27.7%, maximum 52.1% rise) to at least 83,293 annual hospital deaths, mainly due to the increase in hospital deaths in those aged 85+ years. CONCLUSION: In one of the most ageing countries in the world, there is a long standing trend towards hospitalised dying, more pronounced among the oldest old. To meet people's preferences for dying at home, the development of integrated specialist home palliative care teams is needed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".