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Record W2266179496 · doi:10.1001/jama.2015.18603

Comparison of Site of Death, Health Care Utilization, and Hospital Expenditures for Patients Dying With Cancer in 7 Developed Countries

2016· article· en· W2266179496 on OpenAlexafffundabout
Justin E. Bekelman, Scott D. Halpern, Carl Rudolf Blankart, Julie Bynum, Joachim Cohen, Robert Fowler, Stein Kaasa, Lukas Kwietniewski, Hans Olav Melberg, Bregje D. Onwuteaka‐Philipsen, Mariska Oosterveld‐Vlug, Andrew Pring, Jonas Schreyögg, Connie M. Ulrich, Julia Verne, Hannah Wunsch, Ezekiel Emanuel

Bibliographic record

VenueJAMA · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreHealth Sciences Centre
FundersNational Cancer InstituteNational Institute on AgingU.S. Public Health ServiceInstitute for Clinical Evaluative SciencesOrganisation de Coopération et de Développement ÉconomiquesAchmeaCommonwealth Fund
KeywordsMedicineHealth careAcute careEnd-of-life careRetrospective cohort studyEmergency medicineCohortCohort studyPalliative carePlace of deathGerontologyDemographyFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

IMPORTANCE: Differences in utilization and costs of end-of-life care among developed countries are of considerable policy interest. OBJECTIVE: To compare site of death, health care utilization, and hospital expenditures in 7 countries: Belgium, Canada, England, Germany, the Netherlands, Norway, and the United States. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study using administrative and registry data from 2010. Participants were decedents older than 65 years who died with cancer. Secondary analyses included decedents of any age, decedents older than 65 years with lung cancer, and decedents older than 65 years in the United States and Germany from 2012. MAIN OUTCOMES AND MEASURES: Deaths in acute care hospitals, 3 inpatient measures (hospitalizations in acute care hospitals, admissions to intensive care units, and emergency department visits), 1 outpatient measure (chemotherapy episodes), and hospital expenditures paid by insurers (commercial or governmental) during the 180-day and 30-day periods before death. Expenditures were derived from country-specific methods for costing inpatient services. RESULTS: The United States (cohort of decedents aged >65 years, N = 211,816) and the Netherlands (N = 7216) had the lowest proportion of decedents die in acute care hospitals (22.2.% and 29.4%, respectively). A higher proportion of decedents died in acute care hospitals in Belgium (N = 21,054; 51.2%), Canada (N = 20,818; 52.1%), England (N = 97,099; 41.7%), Germany (N = 24,434; 38.3%), and Norway (N = 6636; 44.7%). In the last 180 days of life, 40.3% of US decedents had an intensive care unit admission compared with less than 18% in other reporting nations. In the last 180 days of life, mean per capita hospital expenditures were higher in Canada (US $21,840), Norway (US $19,783), and the United States (US $18,500), intermediate in Germany (US $16,221) and Belgium (US $15,699), and lower in the Netherlands (US $10,936) and England (US $9342). Secondary analyses showed similar results. CONCLUSIONS AND RELEVANCE: Among patients older than 65 years who died with cancer in 7 developed countries in 2010, end-of-life care was more hospital-centric in Belgium, Canada, England, Germany, and Norway than in the Netherlands or the United States. Hospital expenditures near the end of life were higher in the United States, Norway, and Canada, intermediate in Germany and Belgium, and lower in the Netherlands and England. However, intensive care unit admissions were more than twice as common in the United States as in other countries.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.092
GPT teacher head0.434
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations520
Published2016
Admission routes3
Has abstractyes

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