Evidence map›Paper›PMID 39296849›Full record

ArticleFrontiers in public health2024

What drives China's healthcare expenditure? A theoretical and empirical study of determinants and trends.

Zhilin Ge, Jinfeng Cai, Jingbo Hu

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

3 authors.

Zhilin GeSchool of Finance, Jilin University of Finance and Economics, Changchun, China.
Jinfeng CaiBusiness School of Changchun Guanghua University, Changchun, China.
Jingbo HuBusiness School of Changchun Guanghua University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As economic development advances, there is an increasing focus on improving health conditions, making healthcare expenditure a critical issue worldwide. In China, healthcare spending has shown a marked upward trend, highlighting the importance of understanding its underlying determinants to guide effective policy-making. This study introduces the application of an SV-TVP-FAVAR model to examine the drivers of healthcare expenditure in China from 2007 to 2022. The analysis reveals that economic factors, demographic composition, and policy interventions significantly influence healthcare spending dynamics. Economic growth is strongly linked to increased healthcare expenditure, with economic factors having a particularly pronounced impact during periods of prosperity. Although an aging population drives greater demand for healthcare, the growth rate of healthcare spending has not kept pace with demographic aging, especially following China's economic slowdown. Policy variables present a dual-edged impact: while increased fiscal outlays contribute to budget deficits, limiting the fiscal space for healthcare investment, government emphasis on scientific and technological progress tends to enhance healthcare spending, indicating a synergistic relationship between these areas. Furthermore, the study identifies a prolonged impact of the COVID-19 pandemic on healthcare expenditure, which continues to interact with other driving factors over an extended period. The empirical findings from this research provide crucial evidence to support the development of informed healthcare policies.

Indexed as

COVID-19Health ExpendituresChinaEconomic DevelopmentEmpirical ResearchHealth PolicyHumansSARS-CoV-2economichealthcare expenditureimpact factorSV-TVP-FAVAR modeltime-varying

Identifiers

PMID39296849
PMCPMC11408310

What OpenQuestion holds

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LicenceCC BY
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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.