International study of the place of death of people with cancer: a population-level comparison of 14 countries across 4 continents using death certificate data
Bibliographic record
Abstract
BACKGROUND: Where people die can influence a number of indicators of the quality of dying. We aimed to describe the place of death of people with cancer and its associations with clinical, socio-demographic and healthcare supply characteristics in 14 countries. METHODS: Cross-sectional study using death certificate data for all deaths from cancer (ICD-10 codes C00-C97) in 2008 in Belgium, Canada, Czech Republic, England, France, Hungary, Italy, Mexico, the Netherlands, New Zealand, South Korea, Spain (2010), USA (2007) and Wales (N=1,355,910). Multivariable logistic regression analyses evaluated factors associated with home death within countries and differences across countries. RESULTS: Between 12% (South Korea) and 57% (Mexico) of cancer deaths occurred at home; between 26% (Netherlands, New Zealand) and 87% (South Korea) occurred in hospital. The large between-country differences in home or hospital deaths were partly explained by differences in availability of hospital- and long-term care beds and general practitioners. Haematologic rather than solid cancer (odds ratios (ORs) 1.29-3.17) and being married rather than divorced (ORs 1.17-2.54) were most consistently associated with home death across countries. CONCLUSIONS: A large country variation in the place of death can partly be explained by countries' healthcare resources. Country-specific choices regarding the organisation of end-of-life cancer care likely explain an additional part. These findings indicate the further challenge to evaluate how different specific policies can influence place of death patterns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".