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Record W2943458006 · doi:10.1377/hlthaff.2018.05324

China’s Health Expenditure Projections To 2035: Future Trajectory And The Estimated Impact Of Reforms

2019· article· en· W2943458006 on OpenAlexaff
Tiemin Zhai, John Goss, Tania Dmytraczenko, Yuhui Zhang, Jinjing Li, Peipei Chai

Bibliographic record

VenueHealth Affairs · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsChinaPsychological interventionMedicineEnvironmental healthDemographic economicsEconomicsGerontologyGeography

Abstract

fetched live from OpenAlex

To understand the future trajectory of health expenditure in China if current trends continue and the estimated impact of reforms, this study projected health expenditure by disease and function from 2015 to 2035. Current health expenditure in China is projected to grow 8.4 percent annually, on average, in that period. The growth will mainly be driven by rapid increases in services per case of disease and unit cost, which respectively contribute 4.3 and 2.4 percentage points. Circulatory disease expenditure is projected to increase to 23.4 percent of health expenditure by 2035. The biggest challenge facing the Chinese health system is the projected rapid growth in inpatient services. Three percent of gross domestic product could be saved by 2035 by slowing the growth of inpatient service use from 8.2 percent per year in 2016 to 3.5 percent per year in 2035. Health expenditure in 2035 could be reduced by 3.5 percent if the smoking rate were cut in half and by 3.4 percent if the high blood pressure rate were cut by 25 percent. Future action in controlling health expenditure growth in China should focus on the high growth in inpatient services expenditure and interventions to reduce risk factors.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.442
Teacher spread0.414 · 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

Citations38
Published2019
Admission routes1
Has abstractyes

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