Evidence map›Paper›PMID 33673001›Full record

ArticleHealthcare (Basel, Switzerland)2021

Total Health Expenditure and Its Driving Factors in China: A Gray Theory Analysis.

Huanhuan Jia, Hairui Jiang, Jianxing Yu, Jingru Zhang, Peng Cao, Xihe Yu

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Huanhuan JiaSchool of Public Health, Jilin University, Changchun 130000, Jilin Province, China.
Hairui JiangSchool of Public Health, Jilin University, Changchun 130000, Jilin Province, China.
Jianxing YuSchool of Public Health, Jilin University, Changchun 130000, Jilin Province, China.
Jingru ZhangSchool of Public Health, Jilin University, Changchun 130000, Jilin Province, China.
Peng CaoSchool of Public Health, Jilin University, Changchun 130000, Jilin Province, China.
Xihe YuSchool of Public Health, Jilin University, Changchun 130000, Jilin Province, China.

Funding

Humanities and Social Science Fund of Ministry of Education of China No.18YJAZH118
6 · The paper itself

Abstract

The continuous growth in total health expenditure (THE) has become a social issue of common concern in most countries. In China, the total health expenditure (THE) is maintaining a rapid growth trend that is higher than that of the economy, which has become increasingly obvious in the 21st century and has brought a heavy burden to the government and residents. To analyze the main driving factors of THE in China in the 21st century and establish a predictive model, gray system theory was employed to explore the correlation degree between THE and nine hot topics in the areas of the economy, population, health service utilization, and policy using national data from 2000 to 2018. Additionally, a New Structure of the Multivariate Gray Prediction Model of THE was established and compared with the traditional grey model and widely used BP neural network to evaluate the prediction effectiveness of the model. We concluded that the Chinese government and society have played a crucial role in reducing residents' medical burden. Besides this, the improved economy and aging population have increased the demand for health services, leading to the continual increase in THE. Lastly, the improved NSGM(1,N) model achieved good prediction accuracy and has unique advantages in simulating and predicting THE, which can provide a basis for policy formulation.

Indexed as

demographyhealth expenditurepredictions and projectionspublic policysocioeconomic factors

Identifiers

PMID33673001
PMCPMC7918561

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.