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Record W2556047978 · doi:10.1186/s12913-016-1883-7

Does the new cooperative medical scheme reduce inequality in catastrophic health expenditure in rural China?

2016· article· en· W2556047978 on OpenAlexaff
Na Guo, Tor Iversen, Mingshan Lu, Jian Wang, Luwen Shi

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of Calgary
FundersUniversitetet i OsloChina Scholarship CouncilShandong University
KeywordsReimbursementCatastrophic illnessInequalityMedicineHealth careChinaHealth administrationPublic healthIncidence (geometry)Health informaticsEnvironmental healthEconomic growthEconomicsGeographyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: In 2003, the New Cooperative Medical Scheme (NCMS) was introduced in China to re-establish health insurance for the country's vast rural population. In addition, the coverage of NCMS has been expanding after the new health care reform launched in 2009. This study aims to examine whether the NCMS and its recent expansion have reached the goal of reducing the risk and inequality of catastrophic health spending for rural residents in China. METHODS: We conducted a face-to-face household survey in three counties of the Shandong province in 2009 and 2012. Using this unique panel data, we examined the changes in the incidence and intensity of catastrophic health expenditures (CHEs) before and after NCMS reimbursement. We used concentration index (CI) and decomposition method to study the changes in inequality in CHEs. RESULTS: We found that NCMS reimbursement played a role of reducing both the incidence and intensity of CHEs, and that this impact was stronger after the new health care reform was launched. After reimbursement, the concentration indices for CHEs were 0.073 and 0.021 in 2009 and 2012, indicating that the rich had a greater tendency to incur CHEs and there existed less inequality in the incidence of CHEs after reimbursement in 2012 compared with 2009. The decomposition analysis results suggested that changes in CHE inequality between 2009 and 2012 were attributed to changes in economic status and household size rather than reimbursement levels. CONCLUSIONS: Our results indicated that inequality was shrinking from 2009 to 2012, which could be a result of fewer rich people having CHEs in 2012 compared with 2009. The impact of NCMS in alleviating the financial burden of rural residents was still limited, especially among the poor. Health care reform policies in China that aim to reduce CHEs must continue to place an emphasis on improving reimbursement, cost containment, and reducing income inequalities.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.403
Teacher spread0.329 · 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

Citations43
Published2016
Admission routes1
Has abstractyes

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