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Record W100978124

Health care expenditures and ageing: an international comparison.

2003· article· en· W100978124 on OpenAlexaboutno aff
Meena Seshamani, Alastair Gray

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaPopulation ageingHealth economicsDemographicsHealth carePopulation healthPopulationDemographyPublic healthLife expectancyDemographic changeMedicineGeographySocioeconomicsEconomicsEnvironmental healthEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

This study examines national health expenditure trends for Japan, Canada, Australia, and England and Wales (combined) to assess the impact of changing demographics and changing age-specific per capita expenditure on national health expenditure. Age-specific expenditure data were obtained from each country's department of health. We calculated changes in age-specific per capita expenditure, population demographics and the share of expenditures used by the different age groups over time. We then determined the extent to which isolated changes in population growth, demographic shifts and changes in age-specific per capita expenditure could predict observed increases in health expenditure. For Japan, Canada and Australia per capita health expenditure increased fastest among those aged 65 and over, at up to twice the increase of those aged 45-64. In England and Wales, on the other hand, those aged 65 and over experienced one-third of the cost increase of those aged 45-64. Hence, the proportion of national health expenditures used by the population aged 65 and over decreased from 40% to 35% in England and Wales, while increasing in the other countries by up to 10 percentage points. Demographic shifts and population growth predicted only 18% of the observed increases in health care expenditures in England and Wales, compared to 68%, 44% and 34% for Japan, Canada and Australia respectively. These differential changes in costs for older age groups over time invite future research into the driving forces behind these costs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.265
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.454
Teacher spread0.365 · 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 teacher head, 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

Citations27
Published2003
Admission routes1
Has abstractyes

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